Papers with metric recovery algorithms
Gromov-Wasserstein Alignment of Word Embedding Spaces (D18-1)
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| Challenge: | Current unsupervised methods for learning cross-lingual correspondences involve multiple steps, including heuristic post-hoc refinement strategies. |
| Approach: | They propose to cast the correspondence problem directly as an optimal transport problem, building on the idea that word embeddings arise from metric recovery algorithms. |
| Outcome: | The proposed method can be estimated efficiently, requires little or no tuning, and performs comparable with the state-of-the-art in various unsupervised word translation tasks. |